Python for Computational Linear Algebra

Python packages

numpy (numerical linear algebra)
sympy (symbolic & computational linear algebra)

Basic method

`sympy` library

Method	Usage
`.rref()`	Gauss-Jordan eliminations
`.rank()`	Matrix rank
`.col_insert()`	Insert columns
`.row_insert()`	Insert rows
`.det()`	Matrix determinant
`.eigenvals()`	Matrix eigenvalues
`.eigenvects()`	Matrix eigenvactors
`.shape()`	Matrix shape
`@`	Matrix multiplication
`.T`	Transpose matrix
`**(-1)`	Inverse matrix

`numpy.linalg` library

Method	Usage
`.matrix_power(a,n)`	Matrix power
`.eig(a)`	eigenvalues & eigenvetctors
`.eigvals(a)`	eigenvalues only
`.det(a)`	Matrix determonant
`.matrix_rank()`	Matrix rank
`.solve(a, b)`	Solve a linear matrix equation, or system of linear scalar equations
`.inv(a)`	the inverse of a matrix
`.norm()`	Matrix/vector norm